A practical framework for local validation and governance of radiology AI

A new framework proposes that radiology AI should shift from plug-and-play to institution-calibrated stewardship, requiring local validation, monitoring, and multidisciplinary governance to ensure ongoing accuracy, relevance, equity, and utility.

• Radiology AI tools should be locally validated and continuously monitored rather than treated as one-time plug-and-play solutions.
• An institution-specific AI performance profile, analogous to a local antibiogram, is proposed to summarize model performance within the local clinical environment.
• Local calibration, threshold adjustment, and multidisciplinary governance are essential to maintain AI utility over time.

Automated summary

RadPigeon summaries are original and for information only. They are not clinical advice.